Biomedical Text Mining about Alzheimer's Diseases for Machine Reading Evaluation

The paper presents the experiments carried out as part of the participation in the pilot task of Biomedical about Alzheimer for QA4MRE at CLEF 2012. We have submitted total five unique runs in the pilot task. One run uses Term Frequency (TF) of the query words to weight the sentence. Two runs use Term Frequency-Inverted Document Frequency (TF-IDF) of the query words to weight the sentences. The two unique runs differ in the way that when multiple answers get the same scores by our system, we choose the different answer in the different runs. The last two runs use TF or TF-IDF weighting scheme as well as the OMIM terms about Alzheimer for query expansion. Stopwords are removed from the query words and answers. Each sentence in the associated document is assigned a weighting score with respect to query words. The sentence that receives the higher weighting score corresponding to the query words is identified as the more relevant sentence to the document. The corresponding answer option to the given question is scored according to the sentence weighting score and the highest ranked answer is selected as the final answer.